“The growth of players in the market depends on their capabilities to
combine technology with business expertise, data management expertise,
and technology maturation. The shortage of skilled workforce will be a
challenge for market vendors. Intense competition, rapid changes in the
technology, and industry barriers constitute significant risks to vendors”

The global deep learning system market is expected to reach USD
1,325.3 million by 2020, growing at a CAGR of 38.73% during the forecast
period. Deep learning has the potential to be very useful for real-life
applications, due to which it has attracted a lot of attention. In most
of the real-life applications, a large amount of information is gathered
from social media, software service agreements, hardware, website
cookies and app permissions. All this data cannot be used to train
machine learning programs, and it is time-consuming and very expensive.
Deep learning networks are helpful to get very valuable business
information from this data as they excel at unsupervised learning.

Competitive vendor landscape

The global deep
learning system market is highly fragmented with the presence of
many large and small players. Technavio analysts expect market
competition to intensify as different vendors foray into this market.
The multiplicity of vendors with differentiated products has raised the
competition, and vendors continue to innovate in a bid to establish
themselves in the market. In addition, product selection has become more
convoluted with the availability of advanced technologies.

“The growth of players in the market depends on their capabilities to
combine technology with business expertise, data management expertise,
and technology maturation. The shortage of skilled workforce will be a
challenge for market vendors. Intense competition, rapid changes in the
technology, and industry barriers constitute significant risks to vendors,”
says Ishmeet Kaur, a lead enterprise
applicationanalyst from Technavio.

To grow, vendors should provide value-added services such as
consultancy, integration of solutions, support and maintenance, and
end-user training to address available opportunities and risks.

Technavio’s sample reports are free of charge and contain multiple
sections of the report including the market size and forecast, drivers,
challenges, trends, and more.

Top six deep learning system market vendors

Alphabet

In November 2015, Alphabet's Google announced that it is opening its
TensorFlow deep learning framework under the open-source Apache 2.0
license for others to use. TensorFlow is a deep learning framework
developed by Google's Machine Intelligence Research Organization. It is
used for numerical computation with the help of data flow graphs. It
consists of Python APIs and uses data flow graphs for performing
numerical computations. The nodes in the graph represent mathematical
operations and the edges signify multidimensional data array. TensorFlow
has a fast-growing community of users and contributors, which makes it
an important deep learning framework.

BVLC

BVLC provides an environment that facilitates opportunities for
technology transfers and additional sponsored approach to researchers
and implementers. BVLC and community contributors developed deep
learning platform named Caffe under BSD 2-Clause license. Caffe is
developed using C++ programming language with expression, speed, and
modularity in mind. It has various features such as expressive
architecture, extensible code, and fast speed.

Facebook

Facebook provides a deep learning framework Torch, which helps users
train large-scale convolutional neural networks for applications such as
image recognition, AI, and neural network applications. Torch is a
systematic computing framework that offers wide support for machine
learning algorithms. It is built using Lua that runs on Lua (JIT)
compiler. The Tensor libraries that come with it have very efficient
CUDA backend, and neural networks libraries can be used to build random
acyclic computation graphs with automatic differentiation
functionalities.

LISA lab

LISA lab at the University of Montreal developed deep learning framework
named Theano. Theano is a software package or math expression compiler
that efficiently defines, evaluates, and optimizes mathematical
expressions involving multi-dimensional arrays. It allows the user to
write code and compile it on different architectures such as CPU and
GPU. It is not only used for machine learning applications, which are
CPU-intensive but also used for large neural network or deep learning.

Microsoft

The Computational Network Toolkit (CNTK) is an open-source unified
deep-learning toolkit from Microsoft Research. This is used to speed up
developments in AI and makes it easy to combine and train popular deep
learning model types across multiple GPUs and servers. CNTK is used in
many applications such as speech recognition, machine translation, image
captioning, image recognition, language modeling, language
understanding, and text processing and relevance.

Nervana Systems

On August 9, 2016, Intel announced that it is acquiring Nervana Systems,
a deep learning start-up, to strengthen the role of AI solutions within
the company. Nervana developed a Python-based deep learning framework
named Neon. It has recently been open-sourced under an open-source
Apache 2.0 License. Neon has customized CPU and GPU backends, branded as
NervanaCPU and NervanaGPU backends, respectively.

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Technavio analysts employ primary as well as secondary research
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value chain, including vendors, service providers, distributors,
re-sellers, and end-users.

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